Exploratory Data Analysis using SQL Server

This project analyzes a social media platform’s database using advanced SQL queries to extract meaningful insights about user behavior, hashtag trends, engagement patterns, and platform activity. The goal is to simulate a real-world analytics and reporting pipeline for a social networking application.

The project includes:

User Activity Analysis:

Identifying inactive users, users who never post or comment, users who follow no one, and users not followed by anyone.

Engagement Metrics:

Extracting the most liked posts, users who liked or commented on every post (possible bot detection), and average posts per user.

Trend & Hashtag Insights:

Finding the most followed hashtags, trending/most-used hashtags, and posts with the longest captions.

Post & Interaction Statistics:

Counting posts per user, comment frequency, login frequencies per user, and filtering comments containing specific keywords.

These SQL queries collectively provide a comprehensive analytical report similar to what real social media platforms use for decision-making, fraud detection, user engagement monitoring, and trend analysis.